Dictionary word and phrase determination

ABSTRACT

Candidate words in search queries are identified, each candidate word including one or more consecutive characters. For each candidate word, a first count is determined, the first count representing a number of times that the candidate word is the only word in the search queries, and a second count is determined, the second count representing a number of times that the candidate word and one or more other words are included in each of the search queries. One or more of the candidate words are added to an input method editor dictionary based on a relationship between the first count and the second count.

RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/CN2007/001870, titled “DICTIONARY WORD AND PHRASE DETERMINATION”,filed on Jun. 14, 2007, the contents of which are incorporated herein byreference.

BACKGROUND

This disclosure relates to input methods.

Languages that use a logographic script in which one or two characters,for example, glyphs, correspond roughly to one word or meaning have morecharacters than keys on a standard input device, such as a computerkeyboard on a mobile device keypad. For example, the Chinese languagecontains thousands of characters defined by base Pinyin characters andfive tones. The mapping of these many-to-one associations can beimplemented by input methods that facilitate entry of characters andsymbols not found on input devices. Accordingly, a Western-stylekeyboard can be used to input Chinese, Japanese, or Korean characters.In some examples, an input method editor (IME) can be used to search adictionary to find candidate characters, words, or phrases thatcorrespond to the Pinyin characters typed by a user.

SUMMARY

In one aspect, in general, a computer-implemented method includesidentifying candidate words in search queries, each candidate wordincluding one or more consecutive characters, and for each candidateword, determining a first count representing a number of times that thecandidate word is the only word in the search queries, and determining asecond count representing a number of times that the candidate word andone or more other words are included in each of the search queries. Themethod includes adding one or more of the candidate words to an inputmethod editor dictionary based on a relationship between the first countand the second count.

Implementations of the method can include one or more of the followingfeatures. Adding one or more of the candidate words to the input methodeditor dictionary includes adding a candidate word to the input methodeditor dictionary when the first count is larger than the second count.Adding one or more of the candidate words to the input method editordictionary includes adding a candidate word to the input method editordictionary when the first count is larger than the second count and thefirst count is larger than a threshold value. Determining the secondcount includes counting a number of search queries that each includesthe candidate word and one or more other words, in which the candidateword and the one or more other words are separated by one or more whitespaces or punctuation marks entered by users who submitted the searchqueries. The method includes obtaining the search queries from a searchlog. The search log includes search queries submitted by users of asearch service.

In another aspect, in general, an apparatus includes a data store tostore search queries, and a processing device to identify candidatewords in the search queries, each candidate word including one or moreconsecutive characters. For each candidate word, the processing devicedetermines a first count representing a number of times that thecandidate word is the only word in the search queries, and determines asecond count representing a number of times that the candidate word andone or more other words are included in each of the search queries. Theprocessing device adds one or more of the candidate words to an inputmethod editor dictionary based on a relationship between the first countand the second count.

Implementations of the apparatus can include one or more of thefollowing features. The processing device adds a candidate word to theinput method editor dictionary when the first count is larger than thesecond count. The processing device adds a candidate word to the inputmethod editor dictionary when the first count is larger than the secondcount and the first count is larger than a threshold value. Theprocessing engine counts a number of search queries that each includesthe candidate word and one or more other words, in which the candidateword and the one or more other words are separated by one or more whitespaces or punctuation marks entered by users who submitted the searchqueries.

In another aspect, in general, a system includes a data store to storesearch queries, and a processing engine stored in computer readablemedium and including instructions executable by a processing device thatupon such execution cause the processing device to identify candidatewords in the search queries, each candidate word comprising one or moreconsecutive characters. The processing engine includes instructions thatupon execution cause the processing device to, for each candidate word,determine a first count representing a number of times that thecandidate word is the only word in the search queries, and determine asecond count representing a number of times that the candidate word andone or more other words are included in each of the search queries. Theprocessing engine includes instructions that upon execution cause theprocessing device to add one or more of the candidate words to an inputmethod editor dictionary based on a relationship between the first countand the second count.

Implementations of the system can include one or more of the followingfeatures. The processing engine includes instructions executable by theprocessing device and upon such execution cause the processing device toadd a candidate word to the input method editor dictionary when thefirst count is larger than the second count. The processing engineincludes instructions executable by the processing device and upon suchexecution cause the processing device to add a candidate word to theinput method editor dictionary when the first count is larger than thesecond count and the first count is larger than a threshold value. Theprocessing engine includes instructions executable by the processingdevice and upon such execution cause the processing device to count anumber of search queries that each includes the candidate word and oneor more other words, in which the candidate word and the one or moreother words are separated by one or more white spaces or punctuationmarks entered by users who submitted the search queries.

In another aspect, in general, an apparatus includes a dictionary havingwords identified based on a first count representing a number of timesthat the word is the only word in search queries and a second countrepresenting a number of times that the word and one or more other wordsare in each of the search queries. The apparatus includes an inputmethod editor configured to select words from the dictionary.

Implementations of the apparatus can include one or more of thefollowing features. The input method editor includes a Chinese inputmethod editor. The words include Hanzi characters. The search queriesare identified from a search log.

In another aspect, in general, a system includes a data store and aprocessing engine. The data store stores a dictionary that includeswords that are identified based on a first count representing a numberof times that the word is the only word in search queries and a secondcount representing a number of times that the word and one or more otherwords are included in each of the search queries. The processing engineis stored in computer readable medium and includes instructionsexecutable by a processing device that upon such execution cause theprocessing device to provide an input method editor to enable a user toselect words from the dictionary.

In another aspect, in general, a system includes a data store and aprocessing engine. The data store stores a dictionary that includeswords that are identified based on a first count representing a numberof times that the word is the only word in search queries and a secondcount representing a number of times that the word and one or more otherwords are included in each of the search queries. The processing enginecauses a processing device to provide an input method editor to enable auser to select words from the dictionary.

In another aspect, in general, a system includes means for identifyingcandidate words based on a first count representing a number of timesthat the word is the only word in search queries and a second countrepresenting a number of times that the word and one or more other wordsare included in each of the search queries, and means for adding one ormore of the candidate words to an input method editor dictionary.

In another aspect, in general, a computer-implemented method includesidentifying context signals in documents, identifying characters boundedby the context signals, identifying one or more candidate words definedby the characters bounded by the context signals, and adding one or moreof the candidate words to an input method editor dictionary.

Implementations of the method can include one or more of the followingfeatures. Identifying context signals in documents includes identifyingChinese book title marks. Identifying characters bounded by the contextsignals includes identifying Hanzi characters bounded by the contextsignals. The candidate words include Chinese words. Identifying contextsignals in documents includes identifying hypertext markup language tagsin electronic documents. The input method editor dictionary includes aChinese input method editor dictionary. The method includes determininga count of each candidate word. Adding one or more of the candidatewords to the input method editor dictionary includes adding candidatewords having a count that exceeds a threshold to the input method editordictionary. Identifying context signals in documents includesidentifying non-duplicative documents. Determining a count of eachcandidate word includes determining the count of each candidate wordbased on only the non-duplicative documents. The documents include webdocuments obtained from the Internet. The method includes identifyingcandidate words in search queries and adding one or more of thecandidate words to the input method editor dictionary. Identifyingcandidate words in search queries includes, for each candidate word,determining a first count representing a number of times that thecandidate word is the only word in the search queries, and determining asecond count representing a number of times that the candidate word andone or more other words are included in each of the search queries.Identifying candidate words in search queries includes adding one ormore of the candidate words to the input method editor dictionary basedon a relationship between the first count and the second count.

In another aspect, in general, a computer-implemented method includesidentifying pairs of Chinese book title marks in documents, identifyinga candidate word defined by one or more characters marked by each pairof Chinese book title marks, and adding one or more candidate words toan input method editor dictionary.

Implementations of the method can include one or more of the followingfeatures. The Chinese book title marks include single book title marksor double book title marks. The method includes determining a count ofeach candidate word. Adding one or more candidate words to an inputmethod editor dictionary includes adding candidate words having a countthat exceeds a threshold to the input method editor dictionary. Themethod includes identifying candidate words in search queries and addingone or more of the candidate words to the input method editordictionary. Identifying candidate words in search queries includes, foreach candidate word, determining a first count representing a number oftimes that the candidate word is the only word in the search queries,and determining a second count representing a number of times that thecandidate word and one or more other words are included in each of thesearch queries. Identifying candidate words in search queries includesadding one or more of the candidate words to the input method editordictionary based on a relationship between the first count and thesecond count.

In another aspect, in general, a method includes establishing adictionary having words that are identified based on characters boundedby context signals, and providing an input method editor configured toselect words from the dictionary.

Implementations of the method can include one or more of the followingfeatures. Establishing the dictionary includes identifying words basedon characters bounded by Chinese book title marks.

In another aspect, in general, an apparatus includes a dictionary thathas words identified based on candidate words associated with charactersfound in documents, in which each candidate word is associated with oneor more characters enclosed in a pair of Chinese book title marks. Theapparatus includes an input method editor configured to select wordsfrom the dictionary.

Implementations of the apparatus can include one or more of thefollowing features. The candidate words include Hanzi characters. TheChinese book title marks include at least one of single book title marksor double book title marks. The dictionary includes words identifiedbased on a first count representing a number of times that the word isthe only word in search queries and a second count representing a numberof times that the word and one or more other words are in each of thesearch queries.

In another aspect, in general, a system includes a data store and aprocessing engine. The data store stores a document corpus. Theprocessing engine is stored in computer readable medium and includesinstructions executable by a processing device that upon such executioncause the processing device to identify candidate words by findingcharacters in documents of the document corpus in which the charactersare enclosed in pairs of Chinese book title marks, and add one or moreof the candidate words to an input method editor dictionary.

In another aspect, in general, a system includes a data store and theprocessing device. The data store stores a document corpus. Theprocessing device identifies candidate words by finding characters indocuments in the document corpus in which the characters are enclosed inpairs of Chinese book title marks, and adds one or more of the candidatewords to an input method editor dictionary.

In another aspect, in general, a system includes means for identifyingcontext signals in documents, means for identifying characters boundedby the context signals, means for identifying one or more candidatewords defined by the characters bounded by the context signals, andmeans for adding one or more of the candidate words to an input methodeditor dictionary.

In another aspect, in general, a system includes means for identifyingpairs of Chinese book title marks in documents, means for identifying astring of one or more characters bounded by each pair of Chinese booktitle marks, means for identifying a candidate word defined by eachstring of one or more characters, and means for adding one or more ofthe candidate words to an input method editor dictionary.

The systems and methods disclosed herein may have one or more of thefollowing advantages. A dictionary can be automatically established orenhanced based on a corpus of documents and query logs. IME utilizingthe dictionary can provide more accurate identifications of candidatewords for selection. Also, by using the system and method disclosedherein, the dictionary can be efficiently updated, and the speed andefficiency for the computer processing the logographic script, e.g.,Chinese characters, can be improved, and therefore the user's inputspeed of the logographic script can be increased.

The details of one or more embodiments of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example device that can be used toimplement the systems and methods described herein.

FIG. 2 is a block diagram of an example editor system.

FIG. 3 is a diagram of an example input method editor environment.

FIG. 4 is a diagram of an example word and phrase determination engine.

FIG. 5 is a flow diagram of an example process for determining words andphrases based on a document corpus.

FIG. 6 is a flow diagram of an example process for determining words andphrases based on search query logs.

FIG. 7 is a flow diagram of an example process for determining words andphrases.

FIG. 8 is a diagram of an example word and phrase determination engine.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of an example device 100 that can be utilizedto implement the systems and methods described herein. The device 100can, for example, be implemented in a computer device, such as apersonal computer device, or other electronic devices, such as a mobilephone, mobile communication device, personal digital assistant (PDA),and the like.

The example device 100 includes a processing device 102, a first datastore 104, a second data store 106, input devices 108, output devices110, and a network interface 112. A bus system 114, including, forexample, a data bus and a motherboard, can be used to establish andcontrol data communication between the components 102, 104, 106, 108,110 and 112. Other example system architectures can also be used.

The processing device 102 can, for example, include one or moremicroprocessors. The first data store 104 can, for example, include arandom access memory storage device, such as a dynamic random accessmemory, or other types of computer-readable medium memory devices. Thesecond data store 106 can, for example, include one or more hard drives,a flash memory, and/or a read only memory, or other types ofcomputer-readable medium memory devices.

Example input devices 108 can include a keyboard, a mouse, a stylus,etc., and example output devices 110 can include a display device, anaudio device, etc. The network interface 112 can, for example, include awired or wireless network device operable to communicate data to andfrom a network 116. The network 116 can include one or more local areanetworks (LANs) and/or a wide area network (WAN), such as the Internet.

In some implementations, the device 100 can include input method editor(IME) code 101 in a data store, such as the data store 106. The inputmethod editor code 101 can be defined by instructions that uponexecution cause the processing device 102 to carry out input methodediting functions. In an implementation, the input method editor code101 can, for example, include interpreted instructions, such as scriptinstructions, for example, JavaScript or ECMAScript instructions, thatcan be executed in a web browser environment. Other implementations canalso be used, for example, compiled instructions, a stand-aloneapplication, an applet, a plug-in module, etc.

Execution of the input method editor code 101 generates or launches aninput method editor instance 103. The input method editor instance 103can define an input method editor environment, for example, userinterface, and can facilitate the processing of one or more inputmethods at the device 100, during which time the device 100 can receivecomposition inputs for input characters, ideograms, or symbols, such as,for example, Hanzi characters. For example, the user can use one or moreof the input devices 108 (for example, a keyboard, such as aWestern-style keyboard, a stylus with handwriting recognition engines,etc.) to input composition inputs for identification of Hanzicharacters. In some examples, a Hanzi character can be associated withmore than one composition input.

The first data store 104 and/or the second data store 106 can store anassociation of composition inputs and characters. Based on a user input,the input method editor instance 103 can use information in the datastore 104 and/or the data store 106 to identify one or more candidatecharacters represented by the input. In some implementations, if morethan one candidate character is identified, the candidate characters aredisplayed on an output device 110. Using the input device 108, the usercan select from the candidate characters a Hanzi character that the userdesires to input.

In some implementations, the input method editor instance 103 on thedevice 100 can receive one or more Pinyin composition inputs and convertthe composition inputs into Hanzi characters. The input method editorinstance 103 can, for example, use compositions of Pinyin syllables orcharacters received from keystrokes to represent the Hanzi characters.Each Pinyin syllable can, for example, correspond to a key in theWestern style keyboard. Using a Pinyin input method editor, a user caninput a Hanzi character by using composition inputs that include one ormore Pinyin syllables representing the sound of the Hanzi character.Using the Pinyin IME, the user can also input a word that includes twoor more Hanzi characters by using composition inputs that include two ormore Pinyin syllables representing the sound of the Hanzi characters.Input methods for other languages, however, can also be facilitated.

Other application software 105 can also be stored in data stores 104and/or 106, including web browsers, word processing programs, e-mailclients, etc. Each of these applications can generate a correspondingapplication instance 107. Each application instance can define anenvironment that can facilitate a user experience by presenting data tothe user and facilitating data input from the user. For example, webbrowser software can generate a search engine environment; e-mailsoftware can generate an e-mail environment; a word processing programcan generate an editor environment; etc.

In some implementations, a remote computing system 118 having access tothe device 100 can also be used to edit a logographic script. Forexample, the device 100 may be a server that provides logographic scriptediting capability via the network 116. In some examples, a user canedit a logographic script stored in the data store 104 and/or the datastore 106 using a remote computing system, for example, a clientcomputer. The device 100 can, for example, select a character andreceive a composition input from a user over the network interface 112.The processing device 102 can, for example, identify one or morecharacters adjacent to the selected character, and identify one or morecandidate characters based on the received composition input and theadjacent characters. The device 100 can transmit a data communicationthat includes the candidate characters back to the remote computingsystem.

FIG. 2 is a block diagram of an example input method editor system 120.The input method editor system 120 can, for example, be implementedusing the input method editor code 101 and associated data stores 104and 106. The input method editor system 120 includes an input methodeditor engine 122, a dictionary 124, and a composition input table 126.Other storage architectures can also be used. A user can use the IMEsystem 120 to enter, for example, Chinese words or phrases by typingPinyin characters, and the IME engine 122 will search the dictionary 124to identify candidate dictionary entries each including one or moreChinese words or phrases that match the Pinyin characters.

The dictionary 124 includes entries 128 that correspond to characters,words, or phrases of a logographic script used in one or more languagemodels, and characters, words, and phrases in Roman-based orwestern-style alphabets, for example, English, German, Spanish, etc.Each word corresponds to a meaning and may include one or morecharacters. For example, a word (

) having the meaning “apple” includes two Hanzi characters

and

that correspond to Pinyin inputs “ping” and “guo,” respectively. Thecharacter

is also a word that has the meaning “fruit.” The dictionary entries 128may include, for example, idioms (for example,

proper names (for example,

names of historical characters or famous people (for example,

terms of art (for example,

phrases (for example,

book titles (for example,

titles of art works (for example,

or movie titles (for example,

etc., each including one or more characters.

Similarly, the dictionary entries 128 may include, for example, names ofgeographical entities or political entities, names of business concerns,names of educational institutions, names of animals or plants, names ofmachinery, song names, titles of plays, names of software programs,names of consumer products, etc. The dictionary 124 may include, forexample, thousands of characters, words and phrases.

In some implementations, the dictionary 124 includes information aboutrelationships between characters. For example, the dictionary 124 caninclude scores or probability values assigned to a character dependingon other characters adjacent to the character. The dictionary 124 caninclude entry scores or entry probability values each associated withone of the dictionary entries 128 to indicate how often the entry 128 isused in general.

The composition input data store 126 includes an association ofcomposition inputs and the entries 128 stored in the dictionary 124. Insome implementations, the composition input data store 126 can link eachof the entries 128 in the dictionary 124 to a composition input (forexample, Pinyin input) used by the input method editor engine 122. Forexample, the input method editor engine 122 can use the information inthe dictionary 124 and the composition input data store 126 to associateand/or identify one or more entries 128 in the dictionary 124 with oneor more composition inputs in the composition input data store 126.Other associations can also be used.

In some implementations, the candidate selections in the IME system 120can be ranked and presented in the input method editor according to therank.

FIG. 3 is a diagram of an example input method editor environment 300presenting five ranked candidate selections 302. Each candidateselection can be a dictionary entry 128 or a combination of dictionaryentries 128. The candidate selections 302 are identified based on thePinyin inputs 304. A selection indicator 308 surrounds the firstcandidate selection, i.e.,

indicating that the first candidate selection is selected. The user canalso use a number key to select a candidate selection, or use up anddown arrow keys to move the selection indicator 308 to select thecandidate selection.

As described above, the IME engine 122 accesses the dictionary 124 toidentify candidate entries that are associated with Pinyin charactersentered by the user. The dictionary 124 can be updated with new words ornames periodically. For example, names and words that are commonly typedby users of the IME system 120 may change over time in response to newsevents and changes in the society. In some implementations, thedictionary 124 can be established and/or updated based on characters,words, and phrases that are identified from documents and searchqueries.

FIG. 4 is a diagram of an example of a word and phrase determinationengine 400 that identifies dictionary entries 128 (for example, Chinesecharacters, words, and phrases). In some implementations, the engine 400identifies Chinese words and phrases using a context signal baseddetermination engine 406 and/or a query based determination engine 408.The context signal based determination engine 406 processes thedocuments 420 in a document corpus 402 to identify words and phrasesusing context signals. The query based determination engine 408 searchesqueries 418 in the search query logs 404 to identify Chinese words andphrases based on whether the words or phrases appear in the searchqueries alone or in combination with one or more other words or phrases.The identified words and phrases can be merged in a merger engine 414and added as entries 128 to the dictionary 124. In some implementations,only one of the update methods can be used, for example, the dictionary124 can be updated by use of either the document corpus 402 or thesearch query logs 404.

In some implementations, the context signal determination engine 406 isconfigured to determine candidate dictionary entries 422 from thedocuments 420 using context signals that identify bounded content.Example context signals include marks, characters, hypertext mark uplanguage tags, and/or formatting that identify bounded content, such asquotation marks, special identifier characters, underlining, etc.

An example context signal can include Chinese double book title marks,for example,

, and/or Chinese single book title marks, for example, < >. Chinese booktitle marks are commonly used to mark titles or names of documentsand/or cultural works, for example, books, articles, newspapers,journals, and magazines. Chinese book title marks can also be used tomark the titles or names of cultural works such as, for example, songs,movies, television shows, plays, operas, dramas, symphonies, dances,paintings, statutes, and regulations, etc. The book title marks canidentify multiple titles, for example, when a first title includes asecond title, the first title is marked using the double book titlemark, and the second title is marked using the single book title mark.

Chinese book title marks are context signals that mark the boundaries ofwords or phrases. Thus, when one or more characters (for example, Hanzicharacters) appear inside a pair of Chinese book title marks, there is ahigh likelihood that the one or more characters correspond to one ormore words or phrases. The following examples of names or titles ofcultural works being marked by Chinese book title marks areillustrative:

(“Dream of the Red Chamber” book),

(“Upper River During the Qing Ming Festival” painting),

(“Crouching Tiger, Hidden Dragon” movie), and

(“Beethoven's Ninth Symphony”).

The documents 420 can, for example, include documents that can beaccessed over a network. The documents 420 can include, for example, webpages, e-books, journal articles, e-mail messages, advertisements,instant messages, blogs, legal documents, or other types of documents.The document corpus 402 may include documents 420 that cover a widevariety of subjects, such as news, literature, movies, music, politicaldebates, scientific discoveries, legal issues, health issues,environmental issues, etc. The document corpus 402 can be established bygathering the documents 420 from, for example, a local area network or awide area network, such as a corporate Intranet or the public Internet.The number of documents 420 processed can thus be in the range ofmillions of documents, or more. The documents 420 may include, forexample, Hanzi characters, English characters, numbers, punctuationmarks, symbols, HTML codes, etc. Other documents can also be used, forexample, an electronic collection of literary works, an electroniclibrary, etc.

In some implementations, the context signal determination engine 406scans each of the documents 420 to identify pairs of Chinese book titlemarks. For each pair of Chinese book title marks that are identified,the engine 406 identifies a candidate entry 422 defined by a string ofcharacters, for example, one or more Hanzi bounded by the pair ofChinese book title marks, and adds the candidate entry 422 to a firstdictionary 410. The candidate entry 422 may include one or more words orphrases. If a term within a pair of Chinese book title marks isseparated by a punctuation mark, such as a hyphen or colon, the term canbe treated as two separated terms. For example, the engine 406 mayprocess

(the Chinese title for the computer game “Need for Speed: Underground”)and determine that there are two candidate entries 422:

is one candidate entry 422 and

is another candidate entry 422.

Each candidate entry 422 is associated with a count that represents thenumber of occurrences of the candidate entry 422 in the documents 420.In some implementations, the engine 406 is configured such that eachoccurrence of the candidate entry 422 in the same document 420 causesthe count to be increased by one. Thus, for example, if a candidateentry 422 occurs three times in one document 420 and five times inanother document 420, the count for the candidate entry is increased byeight. In some implementations, the engine 406 is configured such thatthe count is increased by one each time a candidate entry 422 occurs ina separate document, regardless of the number of the times that thecandidate entry 422 occurs within each document. In this case, forexample, if the candidate entry 422 occurs three times in one document420 and five times in another document 420, the count associated withthe candidate entry 422 is increased by two.

In some implementations, the engine 406 identifies pairs of Chinese booktitle marks that bound Chinese characters and do not bound characters ofother languages. In this case, if a pair of Chinese book title marksbound a Chinese word and an English word, the Chinese word is notconsidered to be a candidate entry. In some implementations, the engine406 processes the text bound by each pair of Chinese book title marks toremove non-Chinese characters and adds the remaining Chinese charactersas a candidate entry 422 to the first dictionary 410.

In some implementations, the engine 406 sets a range for the number ofcharacters included in each candidate entry 422. For example, the engine406 may require that each candidate entry 422 has at least three Chinesecharacters and not more than ten Chinese characters.

After processing all the documents 420 to identify all the candidateentries 422 that are marked by Chinese book title marks, the engine 406filters the candidate entries 422 to remove the candidate entries withcounts less than a threshold value. In some implementations, thethreshold value can be set between 20 to 40, for example, 30. Thethreshold can, for example, be utilized to remove candidate entries 422that contain errors, have word(s) or phrase(s) that are rarely used, orthat occur infrequently for some other reason.

In some implementations, the query based determination engine 408 isconfigured to identify candidate dictionary entries 416 from searchquery logs 404. The search query logs 404 can include search queries 418submitted by multiple users of one or more search services over a periodof time. The engine 408 identifies candidate entries 416 by findingconsecutive strings of characters in the search queries 418. A searchquery 418 may include one or more candidate entries 416 that areseparated by one or more white spaces or punctuation marks that areentered by a user who submitted the search query 418. For example, asearch query

includes the phrase

(meaning “world's fastest”) and the word

(meaning “supercomputer”) that are separated by a white space. Each ofthe phrase

and the word

is identified by the engine 408 as a candidate entry 416.

In some implementations, the engine 408 assigns two count numbers toeach candidate entry 416, a query count qf and a user-segmented countsf. The query count qf is used to represent the number of times that thecandidate entry 416 is the only word or phrase in the search queries.For example, the query count qf associated with the entry

represents the number of search queries 418 that include only the word

The user-segmented count sf is used to represents the number of searchqueries 418 that each include the candidate entry 416 and one or moreother words or phrases, where the candidate entry 416 and the one ormore other words or phrases can be separated by, for example, one ormore white spaces or punctuation marks entered by users who submittedthe search queries. The candidate entry 416 and the associated querycount qf and user-segmented count sf are stored in a second dictionary412.

For example, if the engine 408 finds a search query 418 that includes

the user-segmented count sf for the candidate entry

is incremented by 1, and the user-segmented count sf for the candidateentry

is also incremented by 1. If the engine 408 finds a search query 418that includes only

the query count qf for the candidate entry

is incremented by 1.

After the engine 408 processes all of the search queries to determineall of the candidate entries 416 and associated query counts qf anduser-segmented counts sf, the engine 408 removes from the dictionary 412candidate entries 416 in which the user-segmented count sf is equal toor greater than the query count sf (i.e., sf≧qf). The engine 408 alsoremoves candidate entries 416 in which the query count qf is less than athreshold value (i.e., qf<threshold). In some implementations, thethreshold value can be set to a value in the range of 3 to 10. Removingcandidate entries having a low query count qf can remove candidateentries 416 that contain errors or are rarely used.

The candidate entries 416 remaining in the dictionary 412 are ones whosequery count qf is greater than the user-segmented count sf (i.e., qf>sf)and has occurred at least a certain number of times in the searchqueries 418 (i.e., qf≧threshold). When the number of times a particularstring of consecutive characters appears by itself in the search queries418 is greater than the number of times that the string appears with oneor more other strings or characters in the search queries 418, there isa high likelihood that the particular string of consecutive characterscorrespond to one or more words or phrases, and is suitable as adictionary entry 128 in the IME dictionary 124.

In some implementations, the engine 400 includes a merger engine 414that merges the dictionary entries 422 and 416 from the first and seconddictionaries 410 and 412, respectively, by removing duplicate dictionaryentries. The non-duplicative dictionary entries are added to the IMEdictionary 124.

FIG. 5 is a flow diagram of an example process 500 for determining wordsand phrases based on a document corpus (for example, document corpus402). The process 500 can, for example, be implemented in a system thatincludes one or more server computers.

The process 500 identifies context signals in documents (502), andidentifies characters bounded by the context signals (504). For example,the context signals can be Chinese book title marks, the characters canbe Hanzi characters, and the documents can be the documents 420 in thedocument corpus 402 of FIG. 4. For example, the engine 406 of FIG. 4 canidentify the context signals and the characters bounded by the contextsignals.

The process 500 identifies one or more candidate words defined by thecharacters bounded by the context signals (506). For example, thecandidate words can be the entries 422 of FIG. 4.

The process 500 adds one or more candidate word to an input methodeditor dictionary (508). For example, the dictionary can be the firstdictionary 410 of FIG. 4 or the IME dictionary 124 of FIG. 2.

FIG. 6 is a flow diagram of an example process 600 for determining wordsand phrases based on search query logs (for example, search query logs404). The process 600 can, for example, be implemented in a system thatincludes one or more server computers.

The process 600 identifies candidate words in search queries, eachcandidate word including one or more consecutive characters (602). Forexample, the characters can be Hanzi characters, the candidate words canbe the entry 416, and the search queries can be the search queries 418of search query logs 404 FIG. 4. For example, the engine 408 canidentify the candidate words in the search queries 418.

For each candidate word, the process 600 determines a first countrepresenting a number of times that the candidate word is the only wordin the search queries (604), and determines a second count representinga number of times that the candidate word and one or more other wordsare included in each of the search queries (606). For example, in eachof the search queries counted by the second count, the candidate wordand the one or more other words can be separated by one or more whitespaces or punctuation marks entered by the user. The engine 408 candetermine the first count and the second count, for example, qf and sf.

After determining all the words have been processed (608), the process600 adds one or more of the candidate words to an input method editordictionary based on a relationship between the first count and thesecond count (610). For example, the dictionary can be the firstdictionary 410 of FIG. 4 or the IME dictionary 124 of FIG. 2. Forexample, the engine 408 may add a candidate word to the dictionary whenthe first count is greater than the second count.

In some implementations, the processes 500 and 600 can be combined andthe words and phrases can be added to a dictionary by a merger process.

FIG. 7 is a flow diagram of an example process 700 for determining wordsand phrases based on a document corpus (for example, document corpus402) and search query logs (for example, search query logs 404). Theprocess 700 can, for example, be implemented in a system that includesone or more server computers. The process 700 includes two processes 722and 724 that can be performed in parallel to generate first and seconddictionaries that are merged into a final dictionary.

The process 722 identifies documents (702). For example, the documentscan be the documents 420 in the document corpus 402 of FIG. 4.

The process 722 identifies pairs of Chinese book title marks in thedocuments 420, and identifies strings of characters marked by the pairsof Chinese book title marks (704). For example, the Chinese book titlemarks can be

or < >, and the string of characters can include Hanzi characters. Forexample, the engine 406 of FIG. 4 can identify the Chinese book titlemarks and strings of characters.

The process 722 designates each string of characters marked by theChinese book title marks as a candidate entry, and adds the candidateentry to a first dictionary (706). The process 722 also associates acount with the candidate entry, in which the count represents the numberof occurrences of the candidate entry in the documents. For example, thefirst dictionary can be the first dictionary 410 of FIG. 4, and theengine 406 can add or update the candidate entries 422 and associatedcounts in the first dictionary 410.

After all the documents have been processed to identify all the pairs ofChinese book title marks, and all the strings of characters marked bythe Chinese book title marks have been added as candidate entries to thefirst dictionary, the process 722 filters the candidate entries in thefirst dictionary by comparing the counts with a threshold value (708).If a count is lower than the threshold value, the candidate entryassociated with the count is removed from the first dictionary. Forexample, the engine 406 can filter the candidate entries 422 in thefirst dictionary 410.

The process 724 identifies search queries (710). For example, the searchqueries can be the search queries 418 of the search logs 404 of FIG. 4.

For each search query, the process 724 identifies a string ofconsecutive characters, or strings of consecutive characters that areseparated by white space(s) or symbol(s) that are not characters, wherethe white space(s) or symbol(s) are entered by the user (712). Forexample, the characters can be Hanzi characters, and the search queriescan be the search queries 418 of FIG. 4. For example, the engine 408 canidentify the string of consecutive characters, or the strings ofconsecutive characters in each of the search queries 418.

The process 724 identifies a candidate entry as being defined by eachstring of consecutive characters, and adds the candidate entry to asecond dictionary (714). The process 724 also associates a query countqf and a user-segmented count sf with each candidate entry. The querycount qf represents the number of search queries that include only thecandidate entry, and the user-segmented count sf represents the numberof search queries that each includes the candidate entry and one or moreother strings of characters.

For example, the candidate entries can be the candidate entries 416 ofFIG. 4, and the second dictionary can be the second dictionary 412. Forexample, the engine 408 can add or update the candidate entries 416 inthe second dictionary 412, and can initialize or update the query countsqf and user-segmented counts sf associated with the candidate entries416.

After all the search queries have been processed and all the strings ofconsecutive characters have been added as candidate entries to thesecond dictionary, the process 724 filters the candidate entries in thesecond dictionary (716). The process 724 compares the query count qf tothe user-segmented count sf, and compares the query count qf to athreshold value. For example, the process 722 removes from the seconddictionary the candidate entries in which the query count qf is lessthan a threshold, and removes candidate entries in which the query countqf is equal to or less than the user-segmented count sf. Afterfiltering, the candidate entries in the second dictionary are ones inwhich the query count qf is greater than the user-segmented count sf,and the query count qf is at least the threshold value. For example,engine 408 filters the candidate entries 416 in the second dictionary412.

After the processes 722 and 724 are completed, each of the first andsecond dictionaries have candidate entries. The process 700 merges thefirst and second dictionaries by removing duplicate candidate entries togenerate a final dictionary (718). The candidate entries in the finaldictionary are added to an IME dictionary (720). For example, the mergerengine 414 of FIG. 4 can be used to merge the first and seconddictionaries 410 and 412, and the candidate entries in the finaldictionary can be added to the IME dictionary 124 of FIG. 2.

In some implementations, rather than using Chinese book title marks toidentify candidate dictionary entries, hypertext markup language (HTML)title tags can be used to identify candidate dictionary entries from webdocuments. For example, a pair of HTML tags <title> and </title> markthe title of an HTML document. A string of characters bounded by the<title> and </title> HTML tags can be identified as a candidatedictionary entry and added to the dictionary 124 if a thresholdcriterion is met (for example, the number of times that the string ofcharacters appear in the web documents is greater than a thresholdvalue).

Although various implementations have been described, otherimplementations can also be used. For example, various forms of theflows shown above may be used, with steps re-ordered, added, or removed.Also, although several implementations and methods have been described,it should be recognized that numerous other implementations arecontemplated. For example, the input engine 122 can be capable ofmapping composition inputs from a western keyboard to input Chinese,Japanese, Korean and/or Indic characters. In some examples, some or allimplementations described can be applicable to other input methods, suchas Cangjie input method, Jiufang input method, Wubi input method, orother input methods. The weight values for different types of documents,and the classification of types of documents, can be different fromthose described above. The number of words and documents beingprocessed, and the sources of the documents in the document corpus 402,can be different from those described above. The processes 722 and 724in FIG. 7 can be performed sequentially. In some implementations, theengine 406 may identify non-duplicative documents 420 in the documentcorpus 402, and identify candidate entries and associated counts basedon the non-duplicative documents. In some implementations, thedictionary 124 can include characters, words, and phrases obtained frompre-existing dictionaries.

In some implementations, the context signal based engine 406 of FIG. 4can be configured such that the count increases as a function of thenumber of times that the candidate entry 422 occurs in each document.For example, the count can be increased by one each time that thecandidate entry 422 occurs in the same document, up to a limit (forexample, three) for each document. Thus, if the upper limit is three andthe candidate entry 422 occurs five times in the same document, thecount is increased by three. For example, the count can be increased asa log function of the number of times that the candidate entry 422occurs within the same document. In some implementations, the engine 406is configured such that the count increases as a function of thelocation where the candidate entry 422 occurs in each document. Forexample, the count can be increased by 1.5 if the candidate entry 422appears in the title of the document 420 (or subject line of an e-mailmessage), and the count can be increased by 1 if the candidate entry 422appears in other places of the document 420. Other methods for modifyingthe count based on occurrences of the candidate entry 422 in thedocuments 420 can also be used.

In some implementations, several dictionaries, for example, a legaldictionary, a medical dictionary, a science dictionary, and a generaldictionary, can be used. Each dictionary can be established by startingwith a dictionary associated with a particular field. The word andphrase determination engine 400 is used to process a document corpushaving documents and search query logs having search queries biasedtoward the field associated with the dictionary. For example, toestablish the probability values of the words in the legal dictionary, adocument corpus having documents and search query logs having searchqueries biased toward the legal field can be used. The IME system 120can allow the user to select the field of interest (for example, legal,medical, science) when entering characters, and the candidate words canbe selected from the dictionary related to the field of interest.

Referring to FIG. 8, in some implementations, the context signal basedengine 406 and the search query based engine 408 write to a singledictionary 800. For example, the engine 406 processes the documents 420and adds or updates candidate entries 802 to the dictionary 800. Eachcandidate entry 802 processed by the engine 406 is associated with adocument occurrence count, representing the number of occurrences of thecandidate entry 802 in the documents 420. The engine 408 processes thesearch queries 418 and adds or updates the candidate entries 802 to thedictionary 800. Each candidate entry 802 processed by the engine 408 isassociated with a query count and a user-segmented count.

After the engines 406 and 408 process all of the documents 420 andsearch queries 418 to determine all of the candidate entries 802 andassociated document occurrence counts, query counts, and user-segmentedcounts, the engine 400 removes from the dictionary 800 the candidateentries 802 in which certain criteria are met, for example: (1) thedocument occurrence count is less than a first threshold value, (2) theuser-segmented count is equal to or greater than the query count, or (3)the query count is less than a second threshold value. The remainingcandidate entries 802 are added to the IME dictionary 124. In someimplementations, the engines 406 and 408 can write to the IME dictionary124 directly, and add, update, or filter the entries 128 in thedictionary 124.

Embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. Embodiments ofthe subject matter described in this specification can be implemented asone or more computer program products, i.e., one or more modules ofcomputer program instructions encoded on a tangible program carrier forexecution by, or to control the operation of, data processing apparatus.The tangible program carrier can be a propagated signal or a computerreadable medium. The propagated signal is an artificially generatedsignal, for example, a machine generated electrical, optical, orelectromagnetic signal, that is generated to encode information fortransmission to suitable receiver apparatus for execution by a computer.The computer readable medium can be a machine readable storage device, amachine readable storage substrate, a memory device, a composition ofmatter effecting a machine readable propagated signal, or a combinationof one or more of them.

The term “data processing apparatus” encompasses all apparatus, devices,and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, for example,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astand alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program does notnecessarily correspond to a file in a file system. A program can bestored in a portion of a file that holds other programs or data (forexample, one or more scripts stored in a markup language document), in asingle file dedicated to the program in question, or in multiplecoordinated files (for example, files that store one or more modules,sub programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, for example, an FPGA (field programmable gate array) or anASIC (application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, forexample, magnetic, magneto optical disks, or optical disks. However, acomputer need not have such devices. Moreover, a computer can beembedded in another device, for example, a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, to name just a few.

Computer readable media suitable for storing computer programinstructions and data include all forms of non volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, for example, EPROM, EEPROM, and flash memory devices; magneticdisks, for example, internal hard disks or removable disks; magnetooptical disks; and CD ROM and DVD ROM disks. The processor and thememory can be supplemented by, or incorporated in, special purpose logiccircuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, for example, a CRT (cathode ray tube) or LCD(liquid crystal display) monitor, for displaying information to the userand a keyboard and a pointing device, for example, a mouse or atrackball, by which the user can provide input to the computer. Otherkinds of devices can be used to provide for interaction with a user aswell; for example, feedback provided to the user can be any form ofsensory feedback, for example, visual feedback, auditory feedback, ortactile feedback; and input from the user can be received in any form,including acoustic, speech, or tactile input.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,for example, as a data server, or that includes a middleware component,for example, an application server, or that includes a front endcomponent, for example, a client computer having a graphical userinterface or a Web browser through which a user can interact with animplementation of the subject matter described is this specification, orany combination of one or more such back end, middleware, or front endcomponents. The components of the system can be interconnected by anyform or medium of digital data communication, for example, acommunication network. Examples of communication networks include alocal area network (“LAN”) and a wide area network (“WAN”), for example,the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or of what may be claimed, but rather as descriptions offeatures that may be specific to particular embodiments of particularinventions. Certain features that are described in this specification inthe context of separate embodiments can also be implemented incombination in a single embodiment. Conversely, various features thatare described in the context of a single embodiment can also beimplemented in multiple embodiments separately or in any suitablesubcombination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can in some cases be excisedfrom the combination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Particular embodiments of the subject matter described in thisspecification have been described. Other embodiments are within thescope of the following claims. For example, the actions recited in theclaims can be performed in a different order and still achieve desirableresults. As one example, the processes depicted in the accompanyingfigures do not necessarily require the particular order shown, orsequential order, to achieve desirable results. In certainimplementations, multitasking and parallel processing may beadvantageous.

1. A computer-implemented method comprising: identifying, using one ormore computers, candidate words in search queries, each candidate wordcomprising one or more consecutive characters; for each candidate word:determining a first count representing a number of times that thecandidate word is the only word in the search queries, and determining asecond count representing a number of times that the candidate word andone or more other words are included in each of the search queries; andadding one or more of the candidate words to an input method editordictionary based on a relationship between the first count and thesecond count.
 2. The method of claim 1 wherein adding one or more of thecandidate words to the input method editor dictionary comprises adding acandidate word to the input method editor dictionary when the firstcount is larger than the second count.
 3. The method of claim 1 whereinadding one or more of the candidate words to the input method editordictionary comprises adding a candidate word to the input method editordictionary when the first count is larger than the second count and thefirst count is larger than a threshold value.
 4. The method of claim 1wherein determining the second count comprises counting a number ofsearch queries that each includes the candidate word and one or moreother words, in which the candidate word and the one or more other wordsare separated by one or more white spaces or punctuation marks enteredby users who submitted the search queries.
 5. The method of claim 1,comprising obtaining the search queries from a search log.
 6. The methodof claim 5 wherein the search log comprises search queries submitted byusers of a search service.
 7. The method of claim 1 wherein thecandidate words comprise Hanzi characters.
 8. The method of claim 1wherein the input method editor dictionary comprises a Chinese inputmethod editor dictionary.
 9. A system, comprising: a data store to storesearch queries; and a processing engine stored in computer readablemedium and comprising instructions executable by a processing devicethat upon such execution cause the processing device to: identifycandidate words in the search queries, each candidate word comprisingone or more consecutive characters; for each candidate word: determine afirst count representing a number of times that the candidate word isthe only word in the search queries, and determine a second countrepresenting a number of times that the candidate word and one or moreother words are included in each of the search queries; and add one ormore of the candidate words to an input method editor dictionary basedon a relationship between the first count and the second count.
 10. Thesystem of claim 9 wherein the processing engine comprises instructionsexecutable by the processing device and upon such execution cause theprocessing device to add a candidate word to the input method editordictionary when the first count is larger than the second count.
 11. Thesystem of claim 9 wherein the processing engine comprises instructionsexecutable by the processing device and upon such execution cause theprocessing device to add a candidate word to the input method editordictionary when the first count is larger than the second count and thefirst count is larger than a threshold value.
 12. The system of claim 9wherein the processing engine comprises instructions executable by theprocessing device and upon such execution cause the processing device tocount a number of search queries that each includes the candidate wordand one or more other words, in which the candidate word and the one ormore other words are separated by one or more white spaces orpunctuation marks entered by users who submitted the search queries. 13.The system of claim 9 wherein the processing engine comprisesinstructions executable by the processing device and upon such executioncause the processing device to obtain the search queries from a searchlog.
 14. The system of claim 13 wherein the search log comprises searchqueries submitted by users of a search service.
 15. The system of claim9 wherein the candidate words comprise Hanzi characters.
 16. Anapparatus comprising: one or more computers operable to provide: adictionary having words identified based on a first count representing anumber of times that the word is the only word in search queries and asecond count representing a number of times that the word and one ormore other words are in each of the search queries; an input methodeditor configured to select words from the dictionary.
 17. The apparatusof claim 16 wherein the input method editor comprises a Chinese inputmethod editor.
 18. The apparatus of claim 16 wherein the words compriseHanzi characters.
 19. The apparatus of claim 16 wherein for each word inthe dictionary, the first count associated with the word is larger thanthe second count associated with the word.
 20. A system comprising:means for identifying candidate words in search queries, each candidateword comprising one or more consecutive characters; means for processingeach candidate word by: determining a first count representing a numberof times that the candidate word is the only word in the search queries,and determining a second count representing a number of times that thecandidate word and one or more other words are included in each of thesearch queries; and means for adding one or more of the candidate wordsto an input method editor dictionary based on a relationship between thefirst count and the second count.
 21. The system of claim 20, comprisingmeans for obtaining the search queries from a search log.
 22. The systemof claim 20 wherein the means for adding one or more of the candidatewords to an input method editor dictionary comprises means for adding acandidate word to the input method editor dictionary when the firstcount is larger than the second count.
 23. The system of claim 20wherein the means for processing each candidate word comprises means fordetermining the second count by counting a number of search queries thateach includes the candidate word and one or more other words, in whichthe candidate word and the one or more other words are separated by oneor more white spaces or punctuation marks entered by users who submittedthe search queries.
 24. A system, comprising: means for identifyingcandidate words based on a first count representing a number of timesthat the word is the only word in search queries and a second countrepresenting a number of times that the word and one or more other wordsare included in each of the search queries; and means for adding one ormore of the candidate words to an input method editor dictionary. 25.The system of claim 24, comprising means for obtaining the searchqueries from a search log.